Bipolar Dissimilarity and Similarity Correlations of Numbers

Abstract

Many papers on fuzzy risk analysis calculate the similarity between fuzzy numbers. Usually, they use symmetric and reflexive similarity measures between parameters of fuzzy sets or “centers of gravity” of generalized fuzzy numbers represented by real numbers. This paper studies bipolar similarity functions (fuzzy relations) defined on a domain with involutive (negation) operation. The bipolarity property reflects a structure of the domain with involutive operation, and bipolar similarity functions are more suitable for calculating a similarity between elements of such domain. On the set of real numbers, similarity measures should take into account symmetry between positive and negative numbers given by involutive negation of numbers. Another reason to consider bipolar similarity functions is that these functions define measures of correlation (association) between elements of the domain. The paper gives a short introduction to the theory of correlation functions defined on sets with an involutive operation. It shows that the dissimilarity function generating Pearson’s correlation coefficient is bipolar. Further, it proposes new normalized similarity and dissimilarity functions on the set of real numbers. It shows that non-bipolar similarity functions have drawbacks in comparison with bipolar similarity functions. For this reason, bipolar similarity measures can be recommended for use in fuzzy risk analysis. Finally, the correlation functions between numbers corresponding to bipolar similarity functions are proposed.

A Novel AHP-PRISM Risk Assessment Method—An Empirical Case Study in a Nuclear Power Plant

“…After a judgment matrix is constructed, a priory vector for weighting the elements of the judgment matrix is calculated based on the normalized eigenvector of the matrix [42]. Then, based on the consistency ratio (CR), the goodness of judgments can be evaluated [34,43] by applying Equation (1).…”

“…In Equation (1), CI is the consistency index of the judgment matrix. The index can be calculated using n compared elements based on Equation (2).…”

“…Risk assessment is a significant process step of risk management, including risk identification and analysis [1]. Many influential and popular risk assessment methods have been developed in recent decades, just like the methodology of FMEA (Failure Mode and Effects Analysis), RM (Risk Matrix), HAZOP (Hazard and Operability Analysis), LOPA (Layers of Protection Analysis), IRIDM (Integrated Risk-Informed Decision-Making), and FTA (Fault Tree Analysis).…”